In very complex and unstructured domains, the Intelligent Decision Support Systems become very important tools for the expert, since allow to manage a quantity of information in a way that would be impossible to do manually. Inside
this kind of systems, the classification tools are one of the most common, and,
specifically, the clustering techniques. However, these techniques have problems
when managing huge amount of variables and classes, because the interpretation
of the generated classes becomes very complicate.
For this reason, in this project we want to generate an automatically conceptual interpretation of the classes generated by a clustering technique to help in
the labor of the expert with a clearer vision of what is representing each class in
order to understand quickly and easy what are the properties and characteristics
of these data.